Model save

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/4a2859cd0d6cd35b1f810c4fd4ad83db1f10fae9/batch_manager.json

@@ -0,0 +1 @@
+{"steps":{"load_data_from_hub_0":"/home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO/53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/4a2859cd0d6cd35b1f810c4fd4ad83db1f10fae9/batch_manager_steps/load_data_from_hub_0/batch_manager_step.json","text_generation_0":"/home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO/53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/4a2859cd0d6cd35b1f810c4fd4ad83db1f10fae9/batch_manager_steps/text_generation_0/batch_manager_step.json"},"last_batch_received":{"text_generation_0":{"seq_no":0,"step_name":"text_generation_0","last_batch":true,"data_hash":"fec9820608752d5ecabb6f2e921f2fa48740c62d","accumulated":false,"created_from":{"load_data_from_hub_0":[[0,10,10]]},"batch_routed_to":[],"size":40,"type_info":{"module":"distilabel.pipeline.batch","name":"_Batch"}},"load_data_from_hub_0":{"seq_no":2,"step_name":"load_data_from_hub_0","last_batch":false,"data_hash":"122d08c31c02ee6d6e9ebb7f1b4554299ca46c69","accumulated":false,"created_from":{},"batch_routed_to":[],"size":50,"type_info":{"module":"distilabel.pipeline.batch","name":"_Batch"}}},"last_batch_sent":{"text_generation_0":{"seq_no":0,"step_name":"text_generation_0","last_batch":false,"data_hash":null,"accumulated":false,"created_from":{"load_data_from_hub_0":[[0,50,50]]},"batch_routed_to":[],"size":0,"type_info":{"module":"distilabel.pipeline.batch","name":"_Batch"}},"load_data_from_hub_0":{"seq_no":2,"step_name":"load_data_from_hub_0","last_batch":false,"data_hash":null,"accumulated":false,"created_from":{},"batch_routed_to":[],"size":0,"type_info":{"module":"distilabel.pipeline.batch","name":"_Batch"}}},"last_batch_flag_sent_to":["text_generation_0"],"received_batch_seq_nos":{"text_generation_0":[],"load_data_from_hub_0":[0,1,2]},"type_info":{"module":"distilabel.pipeline.batch_manager","name":"_BatchManager"}}

53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/4a2859cd0d6cd35b1f810c4fd4ad83db1f10fae9/batch_manager_steps/load_data_from_hub_0/batch_manager_step.json

@@ -0,0 +1 @@
+{"step_name":"load_data_from_hub_0","accumulate":false,"input_batch_size":null,"data":{},"built_batches":[],"seq_no":0,"last_batch_received":[],"convergence_step":true,"convergence_step_batches_consumed":{},"next_expected_created_from_batch_seq_no":0,"next_expected_seq_no":{},"step_signature":"04a0a6d2a56ec3ebeea8e75fb431e9f80fdc7ea6","use_cache":true,"step_offset":{},"type_info":{"module":"distilabel.pipeline.batch_manager","name":"_BatchManagerStep"}}

53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/4a2859cd0d6cd35b1f810c4fd4ad83db1f10fae9/batch_manager_steps/text_generation_0/batch_manager_step.json

@@ -0,0 +1 @@
+{"step_name":"text_generation_0","accumulate":false,"input_batch_size":50,"data":{"load_data_from_hub_0":[{"seq_no":0,"step_name":"load_data_from_hub_0","last_batch":false,"data_hash":"9cfccc8825b8b677766ef3cd02fe7a634b068ead","accumulated":false,"created_from":{},"batch_routed_to":[],"size":50,"type_info":{"module":"distilabel.pipeline.batch","name":"_Batch"}},{"seq_no":1,"step_name":"load_data_from_hub_0","last_batch":false,"data_hash":"e16694c3891d16c01bf6c4f6d41b96f263fef967","accumulated":false,"created_from":{},"batch_routed_to":[],"size":50,"type_info":{"module":"distilabel.pipeline.batch","name":"_Batch"}},{"seq_no":2,"step_name":"load_data_from_hub_0","last_batch":false,"data_hash":"122d08c31c02ee6d6e9ebb7f1b4554299ca46c69","accumulated":false,"created_from":{},"batch_routed_to":[],"size":50,"type_info":{"module":"distilabel.pipeline.batch","name":"_Batch"}}]},"built_batches":[],"seq_no":1,"last_batch_received":[],"convergence_step":false,"convergence_step_batches_consumed":{},"next_expected_created_from_batch_seq_no":0,"next_expected_seq_no":{"load_data_from_hub_0":[1,1]},"step_signature":"0873d6dc3a6f7216415c198a6cfb1587f29dc1ff","use_cache":true,"step_offset":{"load_data_from_hub_0":[0,50]},"type_info":{"module":"distilabel.pipeline.batch_manager","name":"_BatchManagerStep"}}

53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/4a2859cd0d6cd35b1f810c4fd4ad83db1f10fae9/pipeline.log

@@ -0,0 +1,46 @@
+[2025-02-18 20:03:05] INFO     📝 Pipeline data will be written to '/home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO/53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/4a2859cd0d6cd35b1f810c4fd4ad83db1f10fae9/data/steps_outputs'
+[2025-02-18 20:03:05] INFO     ⌛ The steps of the pipeline will be loaded in stages:
+ * Legend: 🚰 GeneratorStep 🌐 GlobalStep 🔄 Step
+ * Stage 0:
+   - 🚰 'load_data_from_hub_0'
+   - 🔄 'text_generation_0' (results cached, won't be loaded and executed)
+[2025-02-18 20:03:05] INFO     ⏳ Waiting for all the steps of stage 0 to load...
+[2025-02-18 20:03:08] INFO     ⏳ Steps from stage 0 loaded: 1/1
+ * 'load_data_from_hub_0' replicas: 1/1
+[2025-02-18 20:03:08] INFO     ✅ All the steps from stage 0 have been loaded!
+[2025-02-18 20:03:08] INFO     🚰 Starting yielding batches from generator step 'load_data_from_hub_0'. Offset: 0
+[2025-02-18 20:03:08] INFO     📨 Step 'load_data_from_hub_0' sending batch 0 to output queue
+[2025-02-18 20:05:06] INFO     🛑 Stopping pipeline. Waiting for steps to finish processing batches...
+[2025-02-18 20:05:06] INFO     🛑 Stopping yielding batches from step 'load_data_from_hub_0'
+[2025-02-18 20:05:06] INFO     🏁 Finished running step 'load_data_from_hub_0' (replica ID: 0)
+[2025-02-18 20:05:07] WARNING  🛑 Press again to force the pipeline to stop.
+[2025-02-18 20:05:18] INFO     💾 Loading `_BatchManager` from cache: '/home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO/53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/4a2859cd0d6cd35b1f810c4fd4ad83db1f10fae9/batch_manager.json'
+[2025-02-18 20:05:18] INFO     📝 Pipeline data will be written to '/home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO/53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/4a2859cd0d6cd35b1f810c4fd4ad83db1f10fae9/data/steps_outputs'
+[2025-02-18 20:05:18] INFO     ⌛ The steps of the pipeline will be loaded in stages:
+ * Legend: 🚰 GeneratorStep 🌐 GlobalStep 🔄 Step
+ * Stage 0:
+   - 🚰 'load_data_from_hub_0'
+   - 🔄 'text_generation_0' (results cached, won't be loaded and executed)
+[2025-02-18 20:05:18] INFO     ⏳ Waiting for all the steps of stage 0 to load...
+[2025-02-18 20:05:21] INFO     ⏳ Steps from stage 0 loaded: 1/1
+ * 'load_data_from_hub_0' replicas: 1/1
+[2025-02-18 20:05:21] INFO     ✅ All the steps from stage 0 have been loaded!
+[2025-02-18 20:05:21] INFO     🚰 Starting yielding batches from generator step 'load_data_from_hub_0'. Offset: 50
+[2025-02-18 20:05:21] INFO     📨 Step 'load_data_from_hub_0' sending batch 1 to output queue
+[2025-02-18 20:06:49] INFO     🛑 Stopping pipeline. Waiting for steps to finish processing batches...
+[2025-02-18 20:06:49] INFO     🛑 Stopping yielding batches from step 'load_data_from_hub_0'
+[2025-02-18 20:06:49] INFO     🏁 Finished running step 'load_data_from_hub_0' (replica ID: 0)
+[2025-02-18 20:06:49] WARNING  🛑 Press again to force the pipeline to stop.
+[2025-02-18 20:13:20] INFO     💾 Loading `_BatchManager` from cache: '/home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO/53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/4a2859cd0d6cd35b1f810c4fd4ad83db1f10fae9/batch_manager.json'
+[2025-02-18 20:13:20] INFO     📝 Pipeline data will be written to '/home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO/53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/4a2859cd0d6cd35b1f810c4fd4ad83db1f10fae9/data/steps_outputs'
+[2025-02-18 20:13:20] INFO     ⌛ The steps of the pipeline will be loaded in stages:
+ * Legend: 🚰 GeneratorStep 🌐 GlobalStep 🔄 Step
+ * Stage 0:
+   - 🚰 'load_data_from_hub_0'
+   - 🔄 'text_generation_0' (results cached, won't be loaded and executed)
+[2025-02-18 20:13:20] INFO     ⏳ Waiting for all the steps of stage 0 to load...
+[2025-02-18 20:13:22] INFO     ⏳ Steps from stage 0 loaded: 1/1
+ * 'load_data_from_hub_0' replicas: 1/1
+[2025-02-18 20:13:22] INFO     ✅ All the steps from stage 0 have been loaded!
+[2025-02-18 20:13:22] INFO     🚰 Starting yielding batches from generator step 'load_data_from_hub_0'. Offset: 100
+[2025-02-18 20:13:22] INFO     📨 Step 'load_data_from_hub_0' sending batch 2 to output queue

53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/4a2859cd0d6cd35b1f810c4fd4ad83db1f10fae9/pipeline.yaml

@@ -0,0 +1,265 @@
+distilabel:
+  version: 1.5.3
+pipeline:
+  name: /home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO
+  description: A pipeline to generate data from a distilled r1 model
+  steps:
+  - step:
+      name: text_generation_0
+      resources:
+        replicas: 1
+        cpus: null
+        gpus: null
+        memory: null
+        resources: null
+      input_mappings:
+        instruction: problem
+      output_mappings: {}
+      use_cache: true
+      input_batch_size: 50
+      llm:
+        cuda_devices: auto
+        disable_cuda_device_placement: false
+        use_magpie_template: false
+        magpie_pre_query_template: null
+        generation_kwargs:
+          temperature: 0.6
+          max_new_tokens: 8192
+        use_offline_batch_generation: false
+        offline_batch_generation_block_until_done: null
+        jobs_ids: null
+        model: /home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO
+        dtype: auto
+        trust_remote_code: false
+        quantization: null
+        revision: null
+        tokenizer: /home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO
+        tokenizer_mode: auto
+        tokenizer_revision: null
+        skip_tokenizer_init: false
+        chat_template: null
+        seed: 0
+        extra_kwargs:
+          tensor_parallel_size: 1
+          max_model_len: 8192
+        structured_output: null
+        type_info:
+          module: distilabel.models.llms.vllm
+          name: vLLM
+      group_generations: false
+      add_raw_output: true
+      add_raw_input: true
+      num_generations: 4
+      use_default_structured_output: false
+      system_prompt: null
+      use_system_prompt: true
+      template: "You will be given a problem. Please reason step by step, and put\
+        \ your final answer within \boxed{}:\n{{ instruction }}"
+      columns:
+      - instruction
+      runtime_parameters_info:
+      - name: resources
+        runtime_parameters_info:
+        - name: replicas
+          optional: true
+          description: The number of replicas for the step.
+        - name: cpus
+          optional: true
+          description: The number of CPUs assigned to each step replica.
+        - name: gpus
+          optional: true
+          description: The number of GPUs assigned to each step replica.
+        - name: memory
+          optional: true
+          description: The memory in bytes required for each step replica.
+        - name: resources
+          optional: true
+          description: A dictionary containing names of custom resources and the number
+            of those resources required for each step replica.
+      - name: input_batch_size
+        optional: true
+        description: The number of rows that will contain the batches processed by
+          the step.
+      - name: llm
+        runtime_parameters_info:
+        - name: cuda_devices
+          optional: true
+          description: A list with the ID of the CUDA devices to be used.
+        - name: disable_cuda_device_placement
+          optional: true
+          description: Whether to disable the CUDA device placement logic or not.
+        - name: generation_kwargs
+          description: The kwargs to be propagated to either `generate` or `agenerate`
+            methods within each `LLM`.
+          keys:
+          - name: max_new_tokens
+            optional: true
+            description: the maximum number of new tokens that the model will generate.  Defaults
+              to `128`.
+          - name: presence_penalty
+            optional: true
+            description: the presence penalty to use for the generation. Defaults
+              to  `0.0`.
+          - name: frequency_penalty
+            optional: true
+            description: the repetition penalty to use for the generation. Defaults  to
+              `0.0`.
+          - name: repetition_penalty
+            optional: true
+            description: the repetition penalty to use for the generation Defaults
+              to  `1.0`.
+          - name: temperature
+            optional: true
+            description: the temperature to use for the generation. Defaults to `0.1`.
+          - name: top_p
+            optional: true
+            description: the top-p value to use for the generation. Defaults to `1.0`.
+          - name: top_k
+            optional: true
+            description: the top-k value to use for the generation. Defaults to `0`.
+          - name: min_p
+            optional: true
+            description: the minimum probability to use for the generation. Defaults
+              to `0.0`.
+          - name: logprobs
+            optional: true
+            description: number of log probabilities to return per output token. If
+              `None`,  then no log probability won't be returned. Defaults to `None`.
+          - name: stop
+            optional: true
+            description: a list of strings that will be used to stop the generation
+              when found.  Defaults to `None`.
+          - name: stop_token_ids
+            optional: true
+            description: a list of token ids that will be used to stop the generation  when
+              found. Defaults to `None`.
+          - name: include_stop_str_in_output
+            optional: true
+            description: whether to include the stop string in the output.  Defaults
+              to `False`.
+          - name: skip_special_tokens
+            optional: true
+            description: whether to exclude special tokens from the output. Defaults  to
+              `False`.
+          - name: logits_processors
+            optional: true
+            description: a list of functions to process the logits before sampling.  Defaults
+              to `None`.
+          - name: extra_sampling_params
+            optional: true
+            description: dictionary with additional arguments to be passed to  the
+              `SamplingParams` class from `vllm`.
+          - name: echo
+            optional: true
+            description: whether to echo the include the prompt in the response or
+              not. Defaults  to `False`.
+        - name: use_offline_batch_generation
+          optional: true
+          description: Whether to use the `offline_batch_generate` method to generate
+            the responses.
+        - name: offline_batch_generation_block_until_done
+          optional: true
+          description: If provided, then polling will be done until the `ofline_batch_generate`
+            method is able to retrieve the results. The value indicate the time to
+            wait between each polling.
+        - name: extra_kwargs
+          optional: true
+          description: 'Additional dictionary of keyword arguments that will be passed
+            to the `vLLM` class of `vllm` library. See all the supported arguments
+            at: https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/llm.py'
+        - name: structured_output
+          optional: true
+          description: The structured output format to use across all the generations.
+      - name: add_raw_output
+        optional: true
+        description: Whether to include the raw output of the LLM in the key `raw_output_<TASK_NAME>`
+          of the `distilabel_metadata` dictionary output column
+      - name: add_raw_input
+        optional: true
+        description: Whether to include the raw input of the LLM in the key `raw_input_<TASK_NAME>`
+          of the `distilabel_metadata` dictionary column
+      - name: num_generations
+        optional: true
+        description: The number of generations to be produced per input.
+      type_info:
+        module: distilabel.steps.tasks.text_generation
+        name: TextGeneration
+    name: text_generation_0
+  - step:
+      name: load_data_from_hub_0
+      resources:
+        replicas: 1
+        cpus: null
+        gpus: null
+        memory: null
+        resources: null
+      input_mappings: {}
+      output_mappings: {}
+      use_cache: true
+      batch_size: 50
+      repo_id: default_name
+      split: train
+      config: null
+      revision: null
+      streaming: false
+      num_examples: 1300
+      storage_options: null
+      runtime_parameters_info:
+      - name: resources
+        runtime_parameters_info:
+        - name: replicas
+          optional: true
+          description: The number of replicas for the step.
+        - name: cpus
+          optional: true
+          description: The number of CPUs assigned to each step replica.
+        - name: gpus
+          optional: true
+          description: The number of GPUs assigned to each step replica.
+        - name: memory
+          optional: true
+          description: The memory in bytes required for each step replica.
+        - name: resources
+          optional: true
+          description: A dictionary containing names of custom resources and the number
+            of those resources required for each step replica.
+      - name: batch_size
+        optional: true
+        description: The number of rows that will contain the batches generated by
+          the step.
+      - name: repo_id
+        optional: false
+        description: The Hugging Face Hub repository ID of the dataset to load.
+      - name: split
+        optional: true
+        description: The split of the dataset to load. Defaults to 'train'.
+      - name: config
+        optional: true
+        description: The configuration of the dataset to load. This is optional and
+          only needed if the dataset has multiple configurations.
+      - name: revision
+        optional: true
+        description: The revision of the dataset to load. Defaults to the latest revision.
+      - name: streaming
+        optional: true
+        description: Whether to load the dataset in streaming mode or not. Defaults
+          to False.
+      - name: num_examples
+        optional: true
+        description: The number of examples to load from the dataset. By default will
+          load all examples.
+      type_info:
+        module: distilabel.steps.generators.huggingface
+        name: LoadDataFromHub
+    name: load_data_from_hub_0
+  connections:
+  - from: text_generation_0
+    to: []
+  - from: load_data_from_hub_0
+    to:
+    - text_generation_0
+  routing_batch_functions: []
+  type_info:
+    module: distilabel.pipeline.local
+    name: Pipeline
+requirements: []

53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/4a2859cd0d6cd35b1f810c4fd4ad83db1f10fae9/stages.json

@@ -0,0 +1 @@
+{"current_stage":0,"stages_last_batch":[[]]}

53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/6a1f0ecd457e3ba74e92592776c6c93fb7e737bd/batch_manager.json

@@ -0,0 +1 @@
+{"steps":{"load_data_from_hub_0":"/home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO/53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/6a1f0ecd457e3ba74e92592776c6c93fb7e737bd/batch_manager_steps/load_data_from_hub_0/batch_manager_step.json","text_generation_0":"/home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO/53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/6a1f0ecd457e3ba74e92592776c6c93fb7e737bd/batch_manager_steps/text_generation_0/batch_manager_step.json"},"last_batch_received":{"text_generation_0":{"seq_no":0,"step_name":"text_generation_0","last_batch":true,"data_hash":"fec9820608752d5ecabb6f2e921f2fa48740c62d","accumulated":false,"created_from":{"load_data_from_hub_0":[[0,10,10]]},"batch_routed_to":[],"size":40,"type_info":{"module":"distilabel.pipeline.batch","name":"_Batch"}},"load_data_from_hub_0":{"seq_no":0,"step_name":"load_data_from_hub_0","last_batch":true,"data_hash":"1376774b81b43f6e0e269cb3187301b22cff8b7e","accumulated":false,"created_from":{},"batch_routed_to":[],"size":10,"type_info":{"module":"distilabel.pipeline.batch","name":"_Batch"}}},"last_batch_sent":{"text_generation_0":{"seq_no":0,"step_name":"text_generation_0","last_batch":true,"data_hash":null,"accumulated":false,"created_from":{"load_data_from_hub_0":[[0,10,10]]},"batch_routed_to":[],"size":0,"type_info":{"module":"distilabel.pipeline.batch","name":"_Batch"}},"load_data_from_hub_0":{"seq_no":0,"step_name":"load_data_from_hub_0","last_batch":false,"data_hash":null,"accumulated":false,"created_from":{},"batch_routed_to":[],"size":0,"type_info":{"module":"distilabel.pipeline.batch","name":"_Batch"}}},"last_batch_flag_sent_to":[],"received_batch_seq_nos":{"text_generation_0":[0],"load_data_from_hub_0":[0]},"type_info":{"module":"distilabel.pipeline.batch_manager","name":"_BatchManager"}}

53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/6a1f0ecd457e3ba74e92592776c6c93fb7e737bd/batch_manager_steps/load_data_from_hub_0/batch_manager_step.json

@@ -0,0 +1 @@
+{"step_name":"load_data_from_hub_0","accumulate":false,"input_batch_size":null,"data":{},"built_batches":[],"seq_no":0,"last_batch_received":[],"convergence_step":true,"convergence_step_batches_consumed":{},"next_expected_created_from_batch_seq_no":0,"next_expected_seq_no":{},"step_signature":"88f19108c6d8fd87f113fbd9b85f4cc1add05dbc","use_cache":true,"step_offset":{},"type_info":{"module":"distilabel.pipeline.batch_manager","name":"_BatchManagerStep"}}

53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/6a1f0ecd457e3ba74e92592776c6c93fb7e737bd/batch_manager_steps/text_generation_0/batch_manager_step.json

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53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/6a1f0ecd457e3ba74e92592776c6c93fb7e737bd/data/steps_outputs/text_generation_0/00001.parquet

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53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/6a1f0ecd457e3ba74e92592776c6c93fb7e737bd/pipeline.log

@@ -0,0 +1,31 @@
+[2025-02-18 19:40:09] INFO     📝 Pipeline data will be written to '/home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO/53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/6a1f0ecd457e3ba74e92592776c6c93fb7e737bd/data/steps_outputs'
+[2025-02-18 19:40:09] INFO     ⌛ The steps of the pipeline will be loaded in stages:
+ * Legend: 🚰 GeneratorStep 🌐 GlobalStep 🔄 Step
+ * Stage 0:
+   - 🚰 'load_data_from_hub_0'
+   - 🔄 'text_generation_0'
+[2025-02-18 19:40:09] INFO     ⏳ Waiting for all the steps of stage 0 to load...
+[2025-02-18 19:40:09] INFO     🎮 LLM 'text_generation_0-replica-0' is going to use the following CUDA devices: [0].
+[2025-02-18 19:40:12] INFO     ⏳ Steps from stage 0 loaded: 1/2
+ * 'text_generation_0' replicas: 0/1
+ * 'load_data_from_hub_0' replicas: 1/1
+[2025-02-18 19:40:59] INFO     ⏳ Steps from stage 0 loaded: 2/2
+ * 'text_generation_0' replicas: 1/1
+ * 'load_data_from_hub_0' replicas: 1/1
+[2025-02-18 19:40:59] INFO     ✅ All the steps from stage 0 have been loaded!
+[2025-02-18 19:40:59] INFO     🚰 Starting yielding batches from generator step 'load_data_from_hub_0'. Offset: 0
+[2025-02-18 19:40:59] INFO     📨 Step 'load_data_from_hub_0' sending batch 0 to output queue
+[2025-02-18 19:40:59] INFO     🏁 Finished running step 'load_data_from_hub_0' (replica ID: 0)
+[2025-02-18 19:40:59] INFO     📦 Processing batch 0 in 'text_generation_0' (replica ID: 0)
+[2025-02-18 19:41:55] INFO     📨 Step 'text_generation_0' sending batch 0 to output queue
+[2025-02-18 19:41:55] INFO     🏁 Finished running step 'text_generation_0' (replica ID: 0)
+[2025-02-18 19:44:10] INFO     💾 Loading `_BatchManager` from cache: '/home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO/53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/6a1f0ecd457e3ba74e92592776c6c93fb7e737bd/batch_manager.json'
+[2025-02-18 19:44:10] INFO     💾 Loaded batch manager from cache doesn't contain any remaining data. Returning `Distiset` from cache data...
+[2025-02-18 19:46:13] INFO     💾 Loading `_BatchManager` from cache: '/home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO/53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/6a1f0ecd457e3ba74e92592776c6c93fb7e737bd/batch_manager.json'
+[2025-02-18 19:46:13] INFO     💾 Loaded batch manager from cache doesn't contain any remaining data. Returning `Distiset` from cache data...
+[2025-02-18 19:48:28] INFO     💾 Loading `_BatchManager` from cache: '/home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO/53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/6a1f0ecd457e3ba74e92592776c6c93fb7e737bd/batch_manager.json'
+[2025-02-18 19:48:28] INFO     💾 Loaded batch manager from cache doesn't contain any remaining data. Returning `Distiset` from cache data...
+[2025-02-18 19:57:16] INFO     💾 Loading `_BatchManager` from cache: '/home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO/53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/6a1f0ecd457e3ba74e92592776c6c93fb7e737bd/batch_manager.json'
+[2025-02-18 19:57:16] INFO     💾 Loaded batch manager from cache doesn't contain any remaining data. Returning `Distiset` from cache data...
+[2025-02-18 19:59:09] INFO     💾 Loading `_BatchManager` from cache: '/home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO/53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/6a1f0ecd457e3ba74e92592776c6c93fb7e737bd/batch_manager.json'
+[2025-02-18 19:59:09] INFO     💾 Loaded batch manager from cache doesn't contain any remaining data. Returning `Distiset` from cache data...

53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/6a1f0ecd457e3ba74e92592776c6c93fb7e737bd/pipeline.yaml

@@ -0,0 +1,265 @@
+distilabel:
+  version: 1.5.3
+pipeline:
+  name: /home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO
+  description: A pipeline to generate data from a distilled r1 model
+  steps:
+  - step:
+      name: text_generation_0
+      resources:
+        replicas: 1
+        cpus: null
+        gpus: null
+        memory: null
+        resources: null
+      input_mappings:
+        instruction: problem
+      output_mappings: {}
+      use_cache: true
+      input_batch_size: 50
+      llm:
+        cuda_devices: auto
+        disable_cuda_device_placement: false
+        use_magpie_template: false
+        magpie_pre_query_template: null
+        generation_kwargs:
+          temperature: 0.6
+          max_new_tokens: 8192
+        use_offline_batch_generation: false
+        offline_batch_generation_block_until_done: null
+        jobs_ids: null
+        model: /home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO
+        dtype: auto
+        trust_remote_code: false
+        quantization: null
+        revision: null
+        tokenizer: /home/jgw/yhx/jiangu-ri/open-r1/data/DeepSeek-R1-Distill-Qwen-1.5B-GRPO
+        tokenizer_mode: auto
+        tokenizer_revision: null
+        skip_tokenizer_init: false
+        chat_template: null
+        seed: 0
+        extra_kwargs:
+          tensor_parallel_size: 1
+          max_model_len: 8192
+        structured_output: null
+        type_info:
+          module: distilabel.models.llms.vllm
+          name: vLLM
+      group_generations: false
+      add_raw_output: true
+      add_raw_input: true
+      num_generations: 4
+      use_default_structured_output: false
+      system_prompt: null
+      use_system_prompt: true
+      template: "You will be given a problem. Please reason step by step, and put\
+        \ your final answer within \boxed{}:\n{{ instruction }}"
+      columns:
+      - instruction
+      runtime_parameters_info:
+      - name: resources
+        runtime_parameters_info:
+        - name: replicas
+          optional: true
+          description: The number of replicas for the step.
+        - name: cpus
+          optional: true
+          description: The number of CPUs assigned to each step replica.
+        - name: gpus
+          optional: true
+          description: The number of GPUs assigned to each step replica.
+        - name: memory
+          optional: true
+          description: The memory in bytes required for each step replica.
+        - name: resources
+          optional: true
+          description: A dictionary containing names of custom resources and the number
+            of those resources required for each step replica.
+      - name: input_batch_size
+        optional: true
+        description: The number of rows that will contain the batches processed by
+          the step.
+      - name: llm
+        runtime_parameters_info:
+        - name: cuda_devices
+          optional: true
+          description: A list with the ID of the CUDA devices to be used.
+        - name: disable_cuda_device_placement
+          optional: true
+          description: Whether to disable the CUDA device placement logic or not.
+        - name: generation_kwargs
+          description: The kwargs to be propagated to either `generate` or `agenerate`
+            methods within each `LLM`.
+          keys:
+          - name: max_new_tokens
+            optional: true
+            description: the maximum number of new tokens that the model will generate.  Defaults
+              to `128`.
+          - name: presence_penalty
+            optional: true
+            description: the presence penalty to use for the generation. Defaults
+              to  `0.0`.
+          - name: frequency_penalty
+            optional: true
+            description: the repetition penalty to use for the generation. Defaults  to
+              `0.0`.
+          - name: repetition_penalty
+            optional: true
+            description: the repetition penalty to use for the generation Defaults
+              to  `1.0`.
+          - name: temperature
+            optional: true
+            description: the temperature to use for the generation. Defaults to `0.1`.
+          - name: top_p
+            optional: true
+            description: the top-p value to use for the generation. Defaults to `1.0`.
+          - name: top_k
+            optional: true
+            description: the top-k value to use for the generation. Defaults to `0`.
+          - name: min_p
+            optional: true
+            description: the minimum probability to use for the generation. Defaults
+              to `0.0`.
+          - name: logprobs
+            optional: true
+            description: number of log probabilities to return per output token. If
+              `None`,  then no log probability won't be returned. Defaults to `None`.
+          - name: stop
+            optional: true
+            description: a list of strings that will be used to stop the generation
+              when found.  Defaults to `None`.
+          - name: stop_token_ids
+            optional: true
+            description: a list of token ids that will be used to stop the generation  when
+              found. Defaults to `None`.
+          - name: include_stop_str_in_output
+            optional: true
+            description: whether to include the stop string in the output.  Defaults
+              to `False`.
+          - name: skip_special_tokens
+            optional: true
+            description: whether to exclude special tokens from the output. Defaults  to
+              `False`.
+          - name: logits_processors
+            optional: true
+            description: a list of functions to process the logits before sampling.  Defaults
+              to `None`.
+          - name: extra_sampling_params
+            optional: true
+            description: dictionary with additional arguments to be passed to  the
+              `SamplingParams` class from `vllm`.
+          - name: echo
+            optional: true
+            description: whether to echo the include the prompt in the response or
+              not. Defaults  to `False`.
+        - name: use_offline_batch_generation
+          optional: true
+          description: Whether to use the `offline_batch_generate` method to generate
+            the responses.
+        - name: offline_batch_generation_block_until_done
+          optional: true
+          description: If provided, then polling will be done until the `ofline_batch_generate`
+            method is able to retrieve the results. The value indicate the time to
+            wait between each polling.
+        - name: extra_kwargs
+          optional: true
+          description: 'Additional dictionary of keyword arguments that will be passed
+            to the `vLLM` class of `vllm` library. See all the supported arguments
+            at: https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/llm.py'
+        - name: structured_output
+          optional: true
+          description: The structured output format to use across all the generations.
+      - name: add_raw_output
+        optional: true
+        description: Whether to include the raw output of the LLM in the key `raw_output_<TASK_NAME>`
+          of the `distilabel_metadata` dictionary output column
+      - name: add_raw_input
+        optional: true
+        description: Whether to include the raw input of the LLM in the key `raw_input_<TASK_NAME>`
+          of the `distilabel_metadata` dictionary column
+      - name: num_generations
+        optional: true
+        description: The number of generations to be produced per input.
+      type_info:
+        module: distilabel.steps.tasks.text_generation
+        name: TextGeneration
+    name: text_generation_0
+  - step:
+      name: load_data_from_hub_0
+      resources:
+        replicas: 1
+        cpus: null
+        gpus: null
+        memory: null
+        resources: null
+      input_mappings: {}
+      output_mappings: {}
+      use_cache: true
+      batch_size: 50
+      repo_id: default_name
+      split: train
+      config: null
+      revision: null
+      streaming: false
+      num_examples: 10
+      storage_options: null
+      runtime_parameters_info:
+      - name: resources
+        runtime_parameters_info:
+        - name: replicas
+          optional: true
+          description: The number of replicas for the step.
+        - name: cpus
+          optional: true
+          description: The number of CPUs assigned to each step replica.
+        - name: gpus
+          optional: true
+          description: The number of GPUs assigned to each step replica.
+        - name: memory
+          optional: true
+          description: The memory in bytes required for each step replica.
+        - name: resources
+          optional: true
+          description: A dictionary containing names of custom resources and the number
+            of those resources required for each step replica.
+      - name: batch_size
+        optional: true
+        description: The number of rows that will contain the batches generated by
+          the step.
+      - name: repo_id
+        optional: false
+        description: The Hugging Face Hub repository ID of the dataset to load.
+      - name: split
+        optional: true
+        description: The split of the dataset to load. Defaults to 'train'.
+      - name: config
+        optional: true
+        description: The configuration of the dataset to load. This is optional and
+          only needed if the dataset has multiple configurations.
+      - name: revision
+        optional: true
+        description: The revision of the dataset to load. Defaults to the latest revision.
+      - name: streaming
+        optional: true
+        description: Whether to load the dataset in streaming mode or not. Defaults
+          to False.
+      - name: num_examples
+        optional: true
+        description: The number of examples to load from the dataset. By default will
+          load all examples.
+      type_info:
+        module: distilabel.steps.generators.huggingface
+        name: LoadDataFromHub
+    name: load_data_from_hub_0
+  connections:
+  - from: text_generation_0
+    to: []
+  - from: load_data_from_hub_0
+    to:
+    - text_generation_0
+  routing_batch_functions: []
+  type_info:
+    module: distilabel.pipeline.local
+    name: Pipeline
+requirements: []

53bedcbb53ca1db3fed4f15c1fb88e4d8a6089c8/executions/6a1f0ecd457e3ba74e92592776c6c93fb7e737bd/stages.json

@@ -0,0 +1 @@
+{"current_stage":0,"stages_last_batch":[["text_generation_0"]]}

README.md

@@ -0,0 +1,68 @@
+---
+base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
+library_name: transformers
+model_name: DeepSeek-R1-Distill-Qwen-1.5B-GRPO
+tags:
+- generated_from_trainer
+- trl
+- grpo
+licence: license
+---
+
+# Model Card for DeepSeek-R1-Distill-Qwen-1.5B-GRPO
+
+This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="QYWH/DeepSeek-R1-Distill-Qwen-1.5B-GRPO", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/2495479412-/huggingface/runs/ll8pde88) 
+
+
+This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
+
+### Framework versions
+
+- TRL: 0.16.0.dev0
+- Transformers: 4.49.0.dev0
+- Pytorch: 2.5.1
+- Datasets: 3.3.0
+- Tokenizers: 0.21.0
+
+## Citations
+
+Cite GRPO as:
+
+```bibtex
+@article{zhihong2024deepseekmath,
+    title        = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
+    author       = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
+    year         = 2024,
+    eprint       = {arXiv:2402.03300},
+}
+
+```
+
+Cite TRL as:
+    
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

all_results.json

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+{
+    "total_flos": 0.0,
+    "train_loss": 0.036479350634126606,
+    "train_runtime": 34004.8654,
+    "train_samples": 1300,
+    "train_samples_per_second": 1.529,
+    "train_steps_per_second": 0.006
+}

config.json

@@ -0,0 +1,30 @@
+{
+  "_name_or_path": "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "max_position_embeddings": 131072,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.49.0.dev0",
+  "use_cache": false,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 151646,
+  "do_sample": true,
+  "eos_token_id": 151643,
+  "temperature": 0.6,
+  "top_p": 0.95,
+  "transformers_version": "4.49.0.dev0"
+}

model.safetensors

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+oid sha256:1e51f376edd035aeedab94a3ff434d2f503da5f1e58c828fe0ffe97e3728bb6f
+size 3554214752

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

steps_data/load_data_from_hub_0_04a0a6d2a56ec3ebeea8e75fb431e9f80fdc7ea6/batch_0.json

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··么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:贞不其受【年】。","solution":"贞问不会得到好年成么？","messages":[{"content":"请将下面的文言文转为白话文:贞不其受【年】。","role":"user"},{"content":"贞问不会得到好年成么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:癸丑卜，争，贞自今至于丁巳我𢦏宙。","solution":"王𰉏日：丁巳我不其𢦏，来甲子𢦏。旬又一日癸亥车弗𢦏。之夕垔，甲子允𢦏。二三四五癸丑日占卜，贞人争问卦，贞问自今(占卜之日)至丁巳日商王(我)要征伐宙方国么？商王看了卜兆以后，亲自判断说：丁巳日商王(我)不征伐宙，于未来的甲子日才去征伐。事后所记应验的结果是十一天之后���癸亥日，商壬之车没有灾伤(卜辞车多为战车意。此处之车，据《合集》11442“车不其氏十朋”看，似应为人名或地名。若此，则应是车地的贵族名车者没有灾伤)。当天夜里天气变坏，于甲子日果然灾伤了(宙)。","messages":[{"content":"请将下面的文言文转为白话文:癸丑卜，争，贞自今至于丁巳我𢦏宙。","role":"user"},{"content":"王𰉏日：丁巳我不其𢦏，来甲子𢦏。旬又一日癸亥车弗𢦏。之夕垔，甲子允𢦏。二三四五癸丑日占卜，贞人争问卦，贞问自今(占卜之日)至丁巳日商王(我)要征伐宙方国么？商王看了卜兆以后，亲自判断说：丁巳日商王(我)不征伐宙，于未来的甲子日才去征伐。事后所记应验的结果是十一天之后的癸亥日，商壬之车没有灾伤(卜辞车多为战车意。此处之车，据《合集》11442“车不其氏十朋”看，似应为人名或地名。若此，则应是车地的贵族名车者没有灾伤)。当天夜里天气变坏，于甲子日果然灾伤了(宙)。","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:叀竝令省廪。","solution":"是命令贵族名竝者去省视仓廪么？","messages":[{"content":"请将下面的文言文转为白话文:叀竝令省廪。","role":"user"},{"content":"是命令贵族名竝者去省视仓廪么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:壬午卜，扶，酒阳甲。","solution":"壬午日占卜，贞人扶问卦，酒祭先王阳甲么？","messages":[{"content":"请将下面的文言文转为白话文:壬午卜，扶，酒阳甲。","role":"user"},{"content":"壬午日占卜，贞人扶问卦，酒祭先王阳甲么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:河𤉲十。","solution":"对先祖神河烧燎之祭十(名、头)为献么？","messages":[{"content":"请将下面的文言文转为白话文:河𤉲十。","role":"user"},{"content":"对先祖神河烧燎之祭十(名、头)为献么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:乙丑卜·","solution":"乙丑日卜问··","messages":[{"content":"请将下面的文言文转为白话文:乙丑卜·","role":"user"},{"content":"乙丑日卜问··","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:癸亥卜，㱿，贞我史𢦏缶。","solution":"癸亥日占卜，贞人㱿问卦，贞问我的史(史为武官)𢦏戕缶(基方)么？","messages":[{"content":"请将下面的文言文转为白话文:癸亥卜，㱿，贞我史𢦏缶。","role":"user"},{"content":"癸亥日占卜，贞人㱿问卦，贞问我的史(史为武官)𢦏戕缶(基方)么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:甲子卜··令众·田，若。","solution":"甲子日占问···令众由民··农田，顺若么？","messages":[{"content":"请将下面的文言文转为白话文:甲子卜··令众·田，若。","role":"user"},{"content":"甲子日占问···令众由民··农田，顺若么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:甲子卜，内，翌乙丑【启】。乙丑···","solution":"甲子日占卜，贞人内问卦，未来的乙丑日(天气晴启)么？乙丑日··","messages":[{"content":"请将下面的文言文转为白话文:甲子卜，内，翌乙丑【启】。乙丑···","role":"user"},{"content":"甲子日占卜，贞人内问卦，未来的乙丑日(天气晴启)么？乙丑日··","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:贞勿令舟。","solution":"贞问不命令方国贵族名舟者么？","messages":[{"content":"请将下面的文言文转为白话文:贞勿令舟。","role":"user"},{"content":"贞问不命令方国贵族名舟者么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:三十牢又羌。","solution":"还是三十对牛和羌奴为献？","messages":[{"content":"请将下面的文言文转为白话文:三十牢又羌。","role":"user"},{"content":"还是三十对牛和羌奴为献？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:丁亥，贞王其汏方𠬝，呼御事。","solution":"丁亥日卜问，商王之(子辈名)汏行方祭用𠬝奴，命令他行事么？","messages":[{"content":"请将下面的文言文转为白话文:丁亥，贞王其汏方𠬝，呼御事。","role":"user"},{"content":"丁亥日卜问，商王之(子辈名)汏行方祭用𠬝奴，命令他行事么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:壬申王勿·不其𰅱。壬申狩𰅱。","solution":"壬申日商壬不·不会有所擒获。壬申日去狩猎，能有擒获。","messages":[{"content":"请将下面的文言文转为白话文:壬申王勿·不其𰅱。壬申狩𰅱。","role":"user"},{"content":"壬申日商壬不·不会有所擒获。壬申日去狩猎，能有擒获。","role":"assistant"}]},{"problem":"请将下面的文言文转��白话文:癸巳卜，令𰅵省廪。","solution":"癸巳日占问，命令贵族𰅵去省视仓廪么？","messages":[{"content":"请将下面的文言文转为白话文:癸巳卜，令𰅵省廪。","role":"user"},{"content":"癸巳日占问，命令贵族𰅵去省视仓廪么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:【】㱿，贞犬延亡𰶤。","solution":"某日占卜，贞人㱿问卦，贞问犬官名延者没有母豕？","messages":[{"content":"请将下面的文言文转为白话文:【】㱿，贞犬延亡𰶤。","role":"user"},{"content":"某日占卜，贞人㱿问卦，贞问犬官名延者没有母豕？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:贞咸弗佐王。","solution":"贞问还是先王大乙不佐助商王呢？","messages":[{"content":"请将下面的文言文转为白话文:贞咸弗佐王。","role":"user"},{"content":"贞问还是先王大乙不佐助商王呢？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:五牛。","solution":"还是五头牛？","messages":[{"content":"请将下面的文言文转为白话文:五牛。","role":"user"},{"content":"还是五头牛？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:贞王飨。","solution":"贞问商王飨宴么？","messages":[{"content":"请将下面的文言文转为白话文:贞王飨。","role":"user"},{"content":"贞问商王飨宴么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:壬辰【卜】，贞王【其】田，亡灾。","solution":"壬辰日占卜，贞问商王去田猎，没有灾祸之事发生吧？","messages":[{"content":"请将下面的文言文转为白话文:壬辰【卜】，贞王【其】田，亡灾。","role":"user"},{"content":"壬辰日占卜，贞问商王去田猎，没有灾祸之事发生吧？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:中日至昃不雨。","solution":"正当中午的时候至太阳偏西的时候，不下雨么？","messages":[{"content":"请将下面的文言文转为白话文:中日至昃不雨。","role":"user"},{"content":"正当中午的时候至太阳偏西的时候，不下雨么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:其𬂤。","solution":"··进行𬂤焚么？","messages":[{"content":"请将下面的文言文转为白话文:其𬂤。","role":"user"},{"content":"··进行𬂤焚么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:戊戌···缶自","solution":"戊戌日··贵族名缶者自么？","messages":[{"content":"请将下面的文言文转为白话文:戊戌···缶自","role":"user"},{"content":"戊戌日··贵族名缶者自么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:贞不其易日。","solution":"贞问还是天气不阴蔽呢？","messages":[{"content":"请将下面的文言文转为白话文:贞不其易日。","role":"user"},{"content":"贞问还是天气不阴蔽呢？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:辛卯卜，侑于伊尹一羌一牢。","solution":"辛卯日卜问，行侑求之祭于旧老名臣伊尹，用一名羌奴，一对牛为献牲么？","messages":[{"content":"请将下面的文言文转为白话文:辛卯卜，侑于伊尹一羌一牢。","role":"user"},{"content":"辛卯日卜问，行侑求之祭于旧老名臣伊尹，用一名羌奴，一对牛为献牲么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:六牛。","solution":"用六牛为献么？此片似不缺字，但“五五”不成辞例。从第(2)辞“六牛”看，第","messages":[{"content":"请将下面的文言文转为白话文:六牛。","role":"user"},{"content":"用六牛为献么？此片似不缺字，但“五五”不成辞例。从第(2)辞“六牛”看，第","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:庚申，贞酒方块御","solution":"庚申日占卜贞问行酒祭··御除灾殃之祭···","messages":[{"content":"请将下面的文言文转为白话文:庚申，贞酒方块御","role":"user"},{"content":"庚申日占卜贞问行酒祭··御除灾殃之祭···","role":"assistant"}]}]],"type_info":{"module":"distilabel.pipeline.batch","name":"_Batch"}}

steps_data/load_data_from_hub_0_04a0a6d2a56ec3ebeea8e75fb431e9f80fdc7ea6/batch_1.json

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nt":"贞问我··么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:贞不其受佑。","solution":"贞问不会受到保佑么？","messages":[{"content":"请将下面的文言文转为白话文:贞不其受佑。","role":"user"},{"content":"贞问不会受到保佑么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:于癸酉延雨。","solution":"在癸酉日雨会绵延不断么？","messages":[{"content":"请将下面的文言文转为白话文:于癸酉延雨。","role":"user"},{"content":"在癸酉日雨会绵延不断么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:驭于之若，王弗每。","solution":"来福吉于此若顺，商王不会晦气不吉���","messages":[{"content":"请将下面的文言文转为白话文:驭于之若，王弗每。","role":"user"},{"content":"来福吉于此若顺，商王不会晦气不吉？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:方块方块【卜】方块，贞于来己亥酒高妣己眔妣庚。","solution":"某日占卜，某贞人问卦【辞残，但从其贞问内容与(1)辞同推知，似亦应为贞人宾于同日卜卦𠁼，贞问于未来的己亥日酒祭先祖之配高妣己及先王之配妣庚么？","messages":[{"content":"请将下面的文言文转为白话文:方块方块【卜】方块，贞于来己亥酒高妣己眔妣庚。","role":"user"},{"content":"某日占卜，某贞人问卦【辞残，但从其贞问内容与(1)辞同推知，似亦应为贞人宾于同日卜卦𠁼，贞问于未来的己亥日酒祭先祖之配高妣己及先王之配妣庚么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:壬寅卜，丁伐彘。","solution":"壬寅日占卜，于名丁先王行杀伐之祭，以彘猪为献？","messages":[{"content":"请将下面的文言文转为白话文:壬寅卜，丁伐彘。","role":"user"},{"content":"壬寅日占卜，于名丁先王行杀伐之祭，以彘猪为献？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:卯卜，行，贞王宾𫩀，亡尤。","solution":"己卯日占卜，贞人行问卦，贞问商王傧于𫩀祭，没有灾忧之事发生吧？(6>方块方块卜，行【贞王】宾兄庚方块，【亡】尤。某日占卜，贞人行问卦，贞问(商王)傧于祭兄庚(即时王祖甲之兄祖庚)之仪，(没有发生)灾忧之事吧？时王祖甲祭其兄祖庚称“兄庚”，可确知此版为第二期祖甲时物。","messages":[{"content":"请将下面的文言文转为白话文:卯卜，行，贞王宾𫩀，亡尤。","role":"user"},{"content":"己卯日占卜，贞人行问卦，贞问商王傧于𫩀祭，没有灾忧之事发生吧？(6>方块方块卜，行【贞王】宾兄庚方块，【亡】尤。某日占卜，贞人行问卦，贞问(商王)傧于祭兄庚(即时王祖甲之兄祖庚)之仪，(没有发生)灾忧之事吧？时王祖甲祭其兄祖庚称“兄庚”，可确知此版为第二期祖甲时物。","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:癸【亥卜】禘【东】。","solution":"癸(亥日卜问)，禘祭(东方之神)么？","messages":[{"content":"请将下面的文言文转为白话文:癸【亥卜】禘【东】。","role":"user"},{"content":"癸(亥日卜问)，禘祭(东方之神)么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:亥有？","solution":"还是亥日有？","messages":[{"content":"请将下面的文言文转为白话文:亥有？","role":"user"},{"content":"还是亥日有？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:弜宾。","solution":"不用傧祭么？","messages":[{"content":"请将下面的文言文转为白话文:弜宾。","role":"user"},{"content":"不用傧祭么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:贞于敦。","solution":"贞问还是在敦这个地方？","messages":[{"content":"请将下面的文言文转为白话文:贞于敦。","role":"user"},{"content":"贞问还是在敦这个地方？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:贞翌甲寅其雨。","solution":"贞问未来的甲寅日下雨么？","messages":[{"content":"请将下面的文言文转为白话文:贞翌甲寅其雨。","role":"user"},{"content":"贞问未来的甲寅日下雨么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:【王】𰉏曰：其有酘。其隹庚吉。其隹·","solution":"商王武丁看了卜兆以后判断说：将有不祥之事发生。是庚日吉利呢？还是【某日吉利】(辞残，据前辞意拟补)？","messages":[{"content":"请将下面的文言文转为白话文:【王】𰉏曰：其有酘。其隹庚吉。其隹·","role":"user"},{"content":"商王武丁看了卜兆以后判断说：将有不祥之事发生。是庚日吉利呢？还是【某日吉利】(辞残，据前辞意拟补)？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:癸巳王卜贞，旬亡祸。王𰉏曰：吉。在十月又二甲午𫩻上甲，祭","solution":"大甲。癸巳日商壬卜问，未来的十大一旬之内没有灾祸之事发生吧？商王看了卜兆，又判断说：吉利！在十二月甲午这一天，用𫩻祭祀先王上甲，用祭祭祀先王大甲。","messages":[{"content":"请将下面的文言文转为白话文:癸巳王卜贞，旬亡祸。王𰉏曰：吉。在十月又二甲午𫩻上甲，祭","role":"user"},{"content":"大甲。癸巳日商壬卜问，未来的十大一旬之内没有灾祸之事发生吧？商王看了卜兆，又判断说：吉利！在十二月甲午这一天，用𫩻祭祀先王上甲，用祭祭祀先王大甲。","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:【贞】勿登五千。","solution":"贞问还是不征集五千兵众？此版为“相间刻辞”，上刻“界划”五处。","messages":[{"content":"请将下面的文言文转为白话文:【贞】勿登五千。","role":"user"},{"content":"贞问还是不征集五千兵众？此版为“相间刻辞”，上刻“界划”五处。","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:于入自日廼𢼊。","solution":"是太阳落时才举行胣裂祭牲之祭？","messages":[{"content":"请将下面的文言文转为白话文:于入自日廼𢼊。","role":"user"},{"content":"是太阳落时才举行胣裂祭牲之祭？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:其自北来雨。","solution":"还是雨从北边过来呢？","messages":[{"content":"请将下面的文言文转为白话文:其自北来雨。","role":"user"},{"content":"还是雨从北边过来呢？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:叀竝𩧷亡灾。","solution":"竝地的𩧷马也没有灾祸之事发生吧？竝地在山西，古代以产名马著称，如古籍里所说“屈产之乘”。","messages":[{"content":"请将下面的文言文转为白话文:叀竝𩧷亡灾。","role":"user"},{"content":"竝地的𩧷马也没有灾祸之事发生吧？竝地在山西，古代以产名马著称，如古籍里所说“屈产之乘”。","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:王𰉏【曰】·","solution":"王看了卜兆以后·(其后字残，所说不可得知)。","messages":[{"content":"请将下面的文言文转为白话文:王𰉏【曰】·","role":"user"},{"content":"王看了卜兆以后·(其后字残，所说不可得知)。","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:翌辛卯不雨。","solution":"未来的辛卯日不下雨么？","messages":[{"content":"请将下面的文言文转为白话文:翌辛卯不雨。","role":"user"},{"content":"未来的辛卯日不下雨么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:弜宾。","solution":"不傧祭？","messages":[{"content":"请将下面的文言文转为白话文:弜宾。","role":"user"},{"content":"不傧祭？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:贞妇好有取不。","solution":"贞问妇好被娶了么？","messages":[{"content":"请将下面的文言文转为白话文:贞妇好有取不。","role":"user"},{"content":"贞问妇好被娶了么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:癸巳于岳。","solution":"癸巳日于先祖神岳行祭么？","messages":[{"content":"请将下面的文言文转为白话文:癸巳于岳。","role":"user"},{"content":"癸巳日于先祖神岳行祭么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:丁丑卜，尹，贞王宾中丁肜，亡尤。","solution":"丁丑日占卜，贞人尹问卦，贞问商王傧于肜祭先王中丁之仪，没有灾忧之事发生吧？","messages":[{"content":"请将下面的文言文转为白话文:丁丑卜，尹，贞王宾中丁肜，亡尤。","role":"user"},{"content":"丁丑日占卜，贞人尹问卦，贞问商王傧于肜祭先王中丁之仪，没有灾忧之事发生吧？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:贞易(赐)牛。","solution":"贞问赏赐牛么？","messages":[{"content":"请将下面的文言文转为白话文:贞易(赐)牛。","role":"user"},{"content":"贞问赏赐牛么？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:于翌甲子步。","solution":"还是在未来的甲子日行步脯之祭呢？","messages":[{"content":"请将下面的文言文转为白话文:于翌甲子步。","role":"user"},{"content":"还是在未来的甲子日行步脯之祭呢？","role":"assistant"}]},{"problem":"请将下面的文言文转为白话文:在濜。","solution":"在濜地么？","messages":[{"content":"请将下面的文言文转为白话文:在濜。","role":"user"},{"content":"在濜地么？","role":"assistant"}]}]],"type_info":{"module":"distilabel.pipeline.batch","name":"_Batch"}}

steps_data/load_data_from_hub_0_04a0a6d2a56ec3ebeea8e75fb431e9f80fdc7ea6/batch_2.json

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贞问，商壬命令贵族名冓者取聚于某方国么？","role":"assistant"}]}]],"type_info":{"module":"distilabel.pipeline.batch","name":"_Batch"}}

steps_data/load_data_from_hub_0_88f19108c6d8fd87f113fbd9b85f4cc1add05dbc/batch_0.json

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tokenizer.json

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+size 11422959

tokenizer_config.json

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+  "padding_side": "left",
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+  "tokenizer_class": "LlamaTokenizerFast",
+  "unk_token": null,
+  "use_default_system_prompt": false
+}

train_results.json

@@ -0,0 +1,8 @@
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